title: A Gaussian Mixed Model for Learning Discrete Bayesian Networks.
abstract: In this paper we address the problem of learning discrete Bayesian networks from noisy data. Considered is a graphical model based on mixture of Gaussian distributions with categorical mixing structure coming from a discrete Bayesian network. The network learning is formulated as a Maximum Likelihood estimation problem and performed by employing an EM algorithm. The proposed approach is relevant to a variety of statistical problems for which Bayesian network models are suitable - from simple regression analysis to learning gene/protein regulatory networks from microarray data.
authors.affiliation: Department of Biostatistics and Computational Biology, University of Rochester Medical Center, 601 Elmwood Ave, Rochester, NY-14642.
authors.forename: Nikolay
authors.initials: N
authors.lastname: Balov